Apache Spark continues to gain a lot of traction as companies launch or expand their big data initiatives. There is no doubt that it’s finding a place in corporate IT strategies.

The open-source cluster computing framework was developed in the AMPLab at the University of California at Berkeley in 2009 and became an incubated project of the Apache Software Foundation in 2013. By early 2014, Spark had become one of the foundation’s top-level projects, and today it is one of the most active projects managed by Apache.

Because Spark was optimized to run in-memory, it is capable of processing data much faster than other approaches such as MapReduce. As a result, Spark can provide much higher performance levels for certain types of applications. By enabling programs to load data into a cluster's memory and query it repeatedly, the framework is ideal for machine learning algorithms.

As with other data processing technologies, Spark is not suitable for all types of workloads. But companies launching big data efforts can leverage the framework for a variety of projects, such as interactive queries across large data sets; the processing of streaming data from sensors, as with Internet of Things (IoT) applications; and machine learning tasks.

In addition, developers can use Spark to support other processing tasks, taking advantage of the open source framework’s huge set of developer libraries and application programming interfaces (APIs) and comprehensive support of popular languages such as Java, Python, R and Scala.

Apache Spark has three key things going for it that IT organizations should keep in mind:

The framework’s relative simplicity. The APIs are designed specifically for interacting easily and rapidly with data at scale, and are structured in such a way that enable application developers to use Spark right away.

The framework is designed for speed, operating both in-memory and on disk. Spark’s performance can be even greater when supporting interactive queries of data stored in memory.

Spark supports multiple programming languages as mentioned above, and it includes native support for tight integration with leading storage solutions in the Hadoop ecosystem and beyond.

Spark is proving to be well suited for a number of business use cases and is helping companies to transform their big data initiatives and deliver analytics much faster and with greater efficiency.

One company, a provider of cloud-based predictive analytics software specifically designed for the telecommunications industry, is using the full Spark stack as part of its Hadoop-based architecture on MapR. This has helped the company achieve horizontal scalability on commodity hardware and reduce storage and computing costs.

The new technology stack allows the software company to continuously innovate and deliver value to its telecommunications customers by offering predictive insights from the cloud. Today’s telecommunications data has higher volumes and frequency and more complex structures, particularly with new types of devices generating data for IoT and the use of mobile phones for a fast-growing number of apps. The company needs to use this data to generate predictive insights using data science and predictive analytics, and Spark helps make this possible.

Another business benefiting from Spark is a global pharmaceuticals manufacturer that relies on big data solutions for drug discovery processes. One of the company’s areas of drug research requires lots of interaction with diverse data from external organizations.

Combined Spark and Hadoop workflow and integration layers enable the company’s researchers to leverage thousands of experiments other organizations have conducted, providing the pharmaceuticals company with a significant competitive advantage. The big data solutions the company uses allows it to integrate and analyze data so that it can speed up drug research.

These technologies are now being used for a variety of projects across the enterprise, including video analysis, proteomics, and meta-genomics. Researchers can access data directly through a Spark API on a number of databases with schemas that are designed for their specific analytics needs.

And a third business use case for Spark comes from a service provider that delivers analytics services to various industries. The company deployed the Spark framework in conjunction with its Hadoop big data initiative, and is able to dramatically cut query times and improve the accuracy of analytics results. That has enabled the company to provide enhanced services to its customers.

Clearly, Apache Spark can provide a number of benefits to organizations looking to get the most value out of their information resources and the biggest returns on their big data investments. The framework provides the speed and efficiency improvements companies need to deliver on the promise of big data and analytics.

Jim has held positions running Operations, Engineering, Architecture and QA teams in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. Jim has built systems that handle more than 50 billion transactions per day and his work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.

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